Robust People Detection and Tracking in a Multi-Camera Indoor Visual Surveillance System

Author

Yang, Tao ; Chen, Francine ; Kimber, Don ; Vaughan, Jim

Author_Institution

FX Palo Alto Lab., Palo Alto

fYear

2007

fDate

2-5 July 2007

Firstpage

675

Lastpage

678

Abstract

In this paper we describe the analysis component of an indoor, real-time, multi-camera surveillance system. The analysis includes: (1) a novel feature-level foreground segmentation method which achieves efficient and reliable segmentation results even under complex conditions, (2) an efficient greedy search based approach for tracking multiple people through occlusion, and (3) a method for multi-camera handoff that associates individual trajectories in adjacent cameras. The analysis is used for an 18 camera surveillance system that has been running continuously in an indoor business over the past several months. Our experiments demonstrate that the processing method for people detection and tracking across multiple cameras is fast and robust.

Keywords

computer graphics; image segmentation; video surveillance; camera surveillance; feature-level foreground segmentation; greedy search; multicamera handoff; multicamera indoor visual surveillance; occlusion; people tracking; realtime surveillance; robust people detection; Business; Cameras; Elevators; Face detection; Image segmentation; Pixel; Real time systems; Robustness; Spatial databases; Surveillance;

fLanguage

English

Publisher

ieee

Conference_Titel

Multimedia and Expo, 2007 IEEE International Conference on

Conference_Location

Beijing

Print_ISBN

1-4244-1016-9

Electronic_ISBN

1-4244-1017-7

Type

conf

DOI

10.1109/ICME.2007.4284740

Filename

4284740